高压电器2025，Vol.61Issue(6)：120-130,137,12.DOI:10.13296/j.1001-1609.hva.2025.06.014
基于CTGAN-CRS与改进卷积神经网络的变压器故障诊断方法
Fault Diagnosis Method of Transformer Based on CTGAN-CRS and Improved Convolutional Neural Network
摘要
Abstract
For improving the fault diagnosis performance of power transformer under the imbalance scenario of dis-solved gas data in oil,a method based on data enhancement and feature augmentation combined with convolutional neural network for transformer fault diagnosis is proposed in this paper.Firstly,a data augmentation method based on conditional tabular generative adversarial network(CTGAN)combined with cascade reject sampling(CRS)is set up to achieve high quality equalization of unbalanced dataset.Then,feature construction and screening method based on an all-type gas ratios with random forests(GRRF)algorithm is constructed to enhance feature dimension and enrich feature diversity.Finally,a fault diagnosis model based on 2D improved convolutional neural network(2D-ICNN)is set up,and the effectiveness of the proposed method is verified through experiments.The results demonstrate that,in comparison to both the oversampling method and CTGAN,the proposed CTGAN-CRS significantly enhances the quality of the generated data.Additionally,the GRRF feature construction method substantially increases feature rich-ness and,on this basis,the fault diagnosis accuracy is improved further by using the improved 2D-ICNN model on this foundation.关键词
变压器故障诊断/数据不平衡/条件式表格生成对抗网络/数据增强/卷积神经网络Key words
transformer fault diagnosis/data imbalance/CTGAN/data augmentation/CNN引用本文复制引用
阎对丰,刘昌林,李元超,王纪儒,孔宪光..基于CTGAN-CRS与改进卷积神经网络的变压器故障诊断方法[J].高压电器,2025,61(6):120-130,137,12.基金项目
国家自然科学基金资助项目(51875432) （51875432）
中国西电集团揭榜挂帅项目(XD207KJ057).Project Supported by National Natural Science Foundation of China(51875432),Unveiling and Leading Project of China XD Group(XD2022K1057). （XD207KJ057）
